Market Context — Why This Technology, Why Now

The global agricultural industry is rapidly adopting smart farming and precision agriculture solutions to enhance efficiency and sustainability. Driven by increasing consumer demand for environmentally friendly produce and stricter regulations on chemical use, there's an urgent need for data-driven pest management. This technology aligns perfectly with these trends, offering a robust tool for optimizing resource allocation and minimizing ecological footprints across the food supply chain.

Key Competitive Advantages
01

Reduces development costs and time by ~66% (1/3 remaining) by eliminating indoor rearing trials, significantly cutting labor, equipment, and multi-year development cycles.

02

Significantly improves prediction accuracy and applicability by directly utilizing real-world field growth and weather observation data, enabling high-precision forecasts relevant to actual environments and broader species.

03

Enhances rapid adaptation to new species by enabling quick prediction model construction for novel arthropod species without indoor rearing, dramatically improving responsiveness to environmental changes.

Market Opportunity
Smart Agriculture Solution Providers
$0.5B–$1.0B globally (AI est.)
Rapidly growing demand for precision agriculture leveraging AI and data drives integration into smart farming platforms.
Smart agriculture platform developers Agricultural software providers IoT solution integrators for farming
Agrochemical & Fertilizer Manufacturers
$1.5B–$2.5B globally (AI est.)
Increased environmental regulations and sustainability demands are driving investment in precise application and control technologies linked to products.
Global agrochemical manufacturers Sustainable fertilizer producers Crop protection technology developers
Large-Scale Farms & Cooperatives
$0.5B–$1.0B globally (AI est.)
Labor shortages and cost reduction pressures create high demand for efficient, scientific pest and disease management systems.
Large-scale corporate farms Agricultural cooperatives Farm management service providers
Food Processing & Distribution
$1.0B–$1.5B globally (AI est.)
Emphasis on supply chain traceability and sustainability increases interest in environmentally friendly production technologies at the farming stage.
Major food processors Food distribution and logistics companies Retail food chains focused on sustainable sourcing
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent establishes a robust intellectual property foundation with 23 claims, covering a broad technical scope. Its clear inventive step was recognized after comparison with prior art, indicating high stability and reduced business risk. The successful prosecution, including overcoming examiner objections, suggests a strong, difficult-to-invalidate patent, supported by expert legal counsel for meticulous claim drafting.

Competitive White Space

This patent primarily covers the prediction methodology. It leaves white space for developing novel sensor hardware for data collection or automated robotic systems for targeted pest control based on the predictions.

Economic Impact
~$1.0M/year estimated pesticide and labor cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Eliminating indoor rearing trials for model construction saves ~$150K/year in labor costs and ~3 years of development time (AI est.). High-precision prediction could reduce pesticide application frequency by an average of 20%, potentially saving ~$50K/year in application costs (AI est.). Reducing crop yield loss due to pest damage by 10% could convert a ~$0.5M annual loss into a ~$50K loss (AI est.).

Speed to Market
4× faster than in-house development
This technology, developed by a national research institution, features an established growth prediction algorithm. Eliminating indoor rearing trials significantly shortens the validation period for new model construction. Licensees can integrate this program with existing weather and field data infrastructure, potentially accelerating market entry by approximately 3 years compared to developing similar technology from scratch, thereby contributing to early business expansion and competitive advantage.
Competitive Positioning

X: Prediction Accuracy & Environmental Adaptability
Y: Ease of Implementation & Cost-Effectiveness

Business Models & Applications
☁️ Prediction SaaS Offering
Provide this program as a cloud-based SaaS, offering farmers easy access to arthropod emergence predictions. A subscription model could ensure stable revenue.
🔗 API Integration Solution
Offer this prediction functionality as an API to existing agricultural management systems and smart farming platforms. Licensees could rapidly integrate high-precision prediction into their services.
📝 Technology Licensing
License this technology to agrochemical and agricultural equipment manufacturers for integration into their products and services, aiming for widespread market adoption and monetization.
Adjacent Application Opportunities
🦐 Aquaculture
Aquaculture Growth & Disease Prediction
By combining water temperature and quality data with growth data for farmed shrimp and fish, this technology could predict growth rates and the emergence of pathogen vectors. This enables optimized rearing management and disease prevention, potentially reducing losses by 10-15%.
🐾 Livestock & Pet Care
Parasite & Disease Vector Prediction
Leveraging environmental data from animal housing, animal health data, and local weather information, this technology could predict the occurrence of arthropod parasites and disease vectors like ticks and flies. This supports proactive control measures, potentially improving livestock health and productivity by 5-10%.
🏥 Public Health & Medical
Infectious Disease Vector Alert System
This technology could predict the emergence of arthropod vectors, such as mosquitoes and ticks that transmit diseases like dengue fever and malaria, using regional weather and historical outbreak data. This enables public health authorities to issue early warnings and plan appropriate control activities, potentially reducing outbreak severity by 20-30%.
Integration Roadmap — Estimated 21-Month Deployment
Data Integration & Infrastructure Setup
Duration: 5 months
Design and build integration interfaces with the licensee's existing weather observation data, field data, and arthropod emergence data. Establish the operational environment for the program.
Model Adjustment & Validation
Duration: 8 months
Adjust program parameters to the licensee's specific environment and target species, then validate model accuracy. Confirm the validity of prediction results through trial operations in actual fields.
Full Operation & Optimization
Duration: 8 months
Optimize the system based on insights from trial operations and commence full-scale deployment. Continuously improve prediction accuracy and maximize effectiveness through ongoing data collection and feedback loops.
Technical Feasibility
This technology is defined as a 'growth prediction program,' primarily implemented as software, making it easily integrable into existing agricultural management systems and smart farming platforms. The patent claims specify generic data input and processing steps independent of specific hardware, allowing licensees to rapidly deploy it using existing data infrastructure without significant capital investment.
Success Scenario
Implementing this technology could optimize pesticide application timing based on high-precision AI predictions, moving beyond traditional experience-based methods. This may reduce annual pesticide application frequency by approximately 20%, lowering costs and environmental impact. Furthermore, it is estimated to reduce crop yield loss due to pest damage by up to 15%, thereby stabilizing annual production and improving profitability.
Patent Record
APPLICATION NO.
特願2024-514508
REGISTRATION NO.
7555167
FILING DATE
2023/05/24
GRANT DATE
2024/09/12
EXPIRATION DATE
2043/05/24
PATENT HOLDER
国立研究開発法人農業・食品産業技術総合研究機構
Examination History
2024年03月05日
早期審査に関する事情説明書
2024年03月05日
出願審査請求書
2024年04月16日
早期審査に関する通知書
2024年06月04日
拒絶理由通知書
2024年07月04日
意見書
2024年07月04日
手続補正書（自発・内容）
2024年08月20日
特許査定